ESM 244 Lab 1

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(here)

## here() starts at /Users/jaleisehall/Desktop/2nd Year Winter Quarter/ESM 244/Labs/esm244-w2021-lab1

library(sf)

## Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1

library(tmap)

Read in SF trees data

sf_trees <- readr::read_csv(here("data", "sf_trees", "sf_trees.csv"))

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   tree_id = col_double(),
##   legal_status = col_character(),
##   species = col_character(),
##   address = col_character(),
##   site_order = col_double(),
##   site_info = col_character(),
##   caretaker = col_character(),
##   date = col_date(format = ""),
##   dbh = col_double(),
##   plot_size = col_character(),
##   latitude = col_double(),
##   longitude = col_double()
## )

Basic wrangling reminders

Refresh some skills for data wrangling & summary statistics using functions in the ‘dplyr’ package.

Find the top 5 highest observations of trees by legal_status, do some wrangling, make a graph.

top_5_status <- sf_trees %>% 
  count(legal_status) %>% 
  drop_na(legal_status) %>% 
  rename(tree_count = n) %>% 
  relocate(tree_count) %>% 
  slice_max(tree_count, n = 5)

Make a graph of those top 5 observations by legal status.

#using fct_reorder here changes the legal status output so that it shows up in the plot in order of lowest to highest tree count
ggplot(data = top_5_status, aes(x = fct_reorder(legal_status, tree_count), y = tree_count)) +
  geom_col() +
  labs(x = "Legal Status",
       y = "Tree Count") +
  coord_flip() +
  theme_minimal()

A few more data wrangling refresher examples

Only want to keep observations (rows) for Blackwood Acacia trees.

blackwood_acacia <- sf_trees %>% 
  filter(str_detect(species, "Blackwood Acacia")) %>% 
  select(legal_status, date, latitude, longitude)

ggplot(data = blackwood_acacia, aes(x = longitude, y = latitude)) +
  geom_point()

## Warning: Removed 27 rows containing missing values (geom_point).

tidyr::separate() and unite() functions

Useful for combining or separating columns.

sf_trees_sep <- sf_trees %>% 
  separate(species, into = c("spp_scientific", "spp_common"), sep = "::")

Example: tidyr::unite()

sf_trees_unite <- sf_trees %>% 
  unite("id_status", tree_id:legal_status, sep = "_cool!_")

Make some actual maps of blackwood acacia trees in SF

st_as_sf() to convert latitude and longitude to spatial coordinates.

blackwood_acacia_sp <- blackwood_acacia %>% 
  drop_na(latitude, longitude) %>% 
  st_as_sf(coords = c("longitude", "latitude"))

# But we need to set the coordinate reference system (CRS) so it's compatible with the street map of San Francisco we'll use as a "base layer":
st_crs(blackwood_acacia_sp) = 4326

ggplot(data = blackwood_acacia_sp) +
  geom_sf(color = "darkgreen") + 
  theme_minimal()

Read in SF roads shapefile:

sf_map <- read_sf(here("data", "sf_map", "tl_2017_06075_roads.shp"))

st_transform(sf_map, 4326)

## Simple feature collection with 4087 features and 4 fields
## geometry type:  LINESTRING
## dimension:      XY
## bbox:           xmin: -122.5136 ymin: 37.70813 xmax: -122.3496 ymax: 37.83213
## geographic CRS: WGS 84
## # A tibble: 4,087 x 5
##    LINEARID   FULLNAME     RTTYP MTFCC                                  geometry
##  * <chr>      <chr>        <chr> <chr>                          <LINESTRING [°]>
##  1 110498938… Hwy 101 S O… M     S1400 (-122.4041 37.74842, -122.404 37.7483, -…
##  2 110498937… Hwy 101 N o… M     S1400 (-122.4744 37.80691, -122.4746 37.80684,…
##  3 110366022… Ludlow Aly … M     S1780 (-122.4596 37.73853, -122.4596 37.73845,…
##  4 110608181… Mission Bay… M     S1400 (-122.3946 37.77082, -122.3929 37.77092,…
##  5 110366689… 25th Ave N   M     S1400 (-122.4858 37.78953, -122.4855 37.78935,…
##  6 110368970… Willard N    M     S1400 (-122.457 37.77817, -122.457 37.77812, -…
##  7 110368970… 25th Ave N   M     S1400 (-122.4858 37.78953, -122.4858 37.78952,…
##  8 110498933… Avenue N     M     S1400 (-122.3643 37.81947, -122.3638 37.82064,…
##  9 110368970… 25th Ave N   M     S1400  (-122.4854 37.78983, -122.4858 37.78953)
## 10 110367749… Mission Bay… M     S1400 (-122.3865 37.77086, -122.3878 37.77076,…
## # … with 4,077 more rows

ggplot(data = sf_map) +
  geom_sf()

Now combine blackwood acacia tree observations & SF roads map:

ggplot() +
  geom_sf(data = sf_map, 
          size = 0.1, 
          color = "darkgray") +
  geom_sf(data = blackwood_acacia_sp, 
          color = "red", 
          size = 0.5) +
  theme_void()

Now an interactive map:

tmap_mode("view")

## tmap mode set to interactive viewing

tm_shape(blackwood_acacia_sp) +
  tm_dots()